An imaging tracker is a device which locates an object in each of a sequence of images. An image is a representation of a two-dimensional signal; the value of the signal at a point in the image is its intensity. In typical applications of an imaging tracker, the image represents the spatially-varying energy intensity reflected or emitted from a scene, and an image sequence is an ordered series of these representations taken at consecutive points in time. Examples would include the reflected light intensity images output by a TV camera, the reflected RF energy level images output by a radar, and the emitted infrared energy level images output by a FLIR (Forward-Looking InfraRed sensor). Many conventional trackers assume that the object intensity in the image is different from the intensity of its surrounding area, and use this property to separate the two. Examples include the centroid tracker, the peak tracker, and the area balance tracker.
Another conventional tracker is the correlation tracker, which saves a subimage of the object and attempts to match this reference subimage to similar-sized subimages at various locations in the new image. The location giving the best match is the object's new location. The correlation tracker uses a point-by-point comparison of the intensities in the two subimages to obtain the best match.
Conventional correlation trackers suffer from several disadvantages. First, the correlation tracker assumes the addition of white Gaussian noise to corrupt the search images. Where the corrupting noise has other than a Gaussian distribution, the performance of the correlation tracker suffers.
Further, correlation tracker performance is degraded by magnification or rotation of the object image due to sensor motion. Also a large amount of data storage and a large number of computations are required by the correlation tracker, as each candidate subimage is compared to the reference subimage pixel by pixel.
The Kolmogorov-Smirnov (K-S) test has been discussed by H. C. Schau in "Kolmogorov-Smirnov Test In Image Processing," Optical Engineering, March/April 1981, Vol. 20, No. 2 (pp. 275-280). In this paper, Schau describes the application of the K-S test to detect an object within an image. Schau discloses the use of the K-S test for image segmentation and mentions in passing the possible application of the K-S test to tracking applications, but does not describe how this could be done.
A need has therefore arisen in the industry for a nonparametric imaging tracker that assumes no distribution for corrupting noise, whose performance is not degraded by magnification or rotation of the object image, and which requires much less data storage and fewer computations.